Object detection is a fundamental problem for numerous vision tasks, including image segmentation, semantic instance segmentation, and detected object reasoning. Detecting all objects in a traffic environment, such as cars, buses, pedestrians, and bicycles, is crucial for building an autonomous driving system. Failure to detect an object (e.g., a car or a person) may lead to malfunction of the motion planning module of an autonomous driving car, thus resulting in a catastrophic accident. As such, object detection for autonomous vehicles is an important operational and safety issue.
Object detection can involve the analysis of images and the use of semantic segmentation on the images. Semantic segmentation aims to assign a categorical label to every pixel in an image, which plays an important role in image analysis and self-driving systems. The semantic segmentation framework provides pixel-level categorical labeling, but no single object-level instance can be discovered. Current object detection frameworks, although useful, cannot recover the shape of the object or deal with the lateral object detection problem. Current technology typically uses two-dimensional bounding boxes applied to images from forward-facing cameras to detect proximate objects, such as other vehicles. However, the angled view of laterally-facing cameras creates a distortion of the images, which degrades the utility and efficiency of the use of bounding boxes for object detection and analysis. As such, a more accurate and efficient detection of lateral objects is needed for autonomous vehicle operation.